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This paper is concerned with the optimal filter problems for networked systems with random transmission delays, while the delay process is modeled as a multi-state Markov chain which incorporates the data losses naturally. By defining an indicator function of the random delay, the optimal filter problems are transformed into the ones of the standard Markov jumping parameter measurement system. We...
In this paper, the Coulomb and the Dahl friction models have been simulated and estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). The estimated friction is used by a controller to compensate and track a desired sinusoidal position profile. In the simulation, the real friction is estimated using EKF and UKF with the Coulomb and the Dahl friction models. The designed controller...
The interactions between subsystems are important for large-scale systems. We introduce a local strongly coupled system which coupled by random communication between subsystems.Due to the intermittent communication, it is difficult to apply the standard Kalman or robust filter to design procedures to such systems. In this paper, we addressed the distributed robust filter design method for this kind...
In this paper, we address with the problem of detection the number of narrowband signals impinging on a uniform linear array (ULA) in the presence of multipath propagation. Firstly, by forming a differencing matrix from the array covariance matrix to eliminate the effects of uncorrelated incident signals and additive noises, a combined matrix is constructed from this differencing matrix to decorrelate...
This overview paper reviews covariance estimation problems and related issues arising in the context of portfolio optimization. Given several assets, a portfolio optimizer seeks to allocate a fixed amount of capital among these assets so as to optimize some cost function. For example, the classical Markowitz portfolio optimization framework defines portfolio risk as the variance of the portfolio return,...
We present a new method for DOA estimation that relies on the assumption that the sources can be modelled as ARMA. A matrix formulation is introduced in the frequency domain so that the available information from the sources can be incorporated thus allowing the DOA estimation with a few sensors and achieve good estimates with acceptable computational demand. Due to the nature of the ARMA models we...
An improved estimator of certain bilinear forms of the logarithm of the covariance matrix is presented. The new estimator is shown to be consistent, not only for increasing sample size (as traditional estimators), but also when the observation dimension scales up at the same rate as the number of available observations. This characteristic provides very good properties whenever the sample volume and...
Estimation of Distribution Algorithms (EDAs) focus on explicitly modelling dependencies between solution variables. A Gaussian distribution over continuous variables is commonly used, with several different covariance matrix structures ranging from diagonal i.e. Univariate Marginal Distribution Algorithm (UMDAc) to full i.e. Estimation of Multivariate Normal density Algorithm (EMNA). A diagonal covariance...
Modern electric machines are required to have the best possible dynamic performances. In induction machines this is achieved by control strategies that are applied with respect to the flux in the air gap and therefore they require precise information on flux position. This paper proposes an observer with autotuning capability that uses the unscented Kalman filter algorithm for providing on-line estimation...
In this paper, multiple input multiple output (MIMO) channel estimation formulated as a chance constrained problem is investigated. The chance constraint is based on the presumption that the estimated channel can be used in an application to achieve a given performance level with a prescribed probability. The aforementioned performance level is dictated by the particular application of interest. The...
In this paper, we propose a new class of lower bounds on the mean-squared error (MSE) in non-Bayesian constrained parameter estimation. The new class includes lower bounds on the MSE of any constrained-unbiased estimator, where the constrained-unbiasedness is defined for the first time using the Lehmann-unbiasedness. The proposed class of constrained lower bounds is derived by employing Cauchy-Schwarz...
We consider large scale covariance estimation using a small number of samples in applications where there is a natural ordering between the random variables. The two classical approaches to this problem rely on banded covariance and banded inverse covariance structures, corresponding to time varying moving average (MA) and autoregressive (AR) models, respectively. Motivated by this analogy to spectral...
TheMUSIC-group delay spectrum has been hitherto used for high resolution direction of arrival estimation using a limited number of sensors. In this work we discuss the significance of the MUSIC-group delay spectrum for robust direction of arrival estimation using shrinkage estimators. The MUSIC-group delay spectrum computed using shrinkage estimation of the covariance matrix, is found to be robust...
This paper proposes a LMMSE-based channel estimator which, unlike the classical LMMSE estimator, does not require the covariance matrix of the channel nor its estimation. Actually, we add at the receiver side a fully adjustable filter, which acts like an artificial channel and hides the physical channel. We then perform an LMMSE estimation of the sum of the physical and artificial channel using the...
State of the art visual odometry systems use bundle adjustment (BA) like methods to jointly optimize motion and scene structure. Fusing measurements from multiple time steps and optimizing an error criterion in a batch fashion seems to deliver the most accurate results. However, often the scene structure is of no interest and is a mere auxiliary quantity although it contributes heavily to the complexity...
In this paper we exploit the robust direct data domain STAP (RD3-STAP) in the SAR case. This is extremely interesting especially for very high resolution SAR systems. In fact, in this case due to the strong heterogeneous characteristics of the clutter over range, stochastic STAP might fail due to the possible lack of enough homogeneous secondary data for adequate clutter covariance matrix estimation...
Based on Simulink/Modelsim co-simulation technology, the design of a sensorless control IP (Intellectual Property) using reduced-order EKF (Extended Kalman Filter) for PMSM (Permanent Magnet Synchronous Motor) drive is presented in this paper. Firstly, a mathematical model for PMSM is derived and the vector control is adopted. Secondly, the rotor flux position and rotor speed are estimated by using...
Traditional detection approaches for the dim target are analyzed under the background of homogeneous clutter. However, the realistic clutter scenarios commonly appear inhomogeneous. In this paper, an adaptive multiple-scan procedure is proposed to detect the weak target in the heterogeneous compound-Gaussian clutter. Numerical simulation results are provided to illustrate the efficiency of the proposed...
Based on the spatial smoothing technique, two decorrelation schemes are proposed for sparse array to handle the super resolution direction of arrival (DOA) estimation problem of coherent sources when using such an array. The first scheme performs with smaller correlation window but lager number of smoothing time, whereas the second one has the opposite characteristic. The direction finding performances...
A fast and efficient algorithm is proposed to estimate the direction of arrival (DOA) of the signals impinging on an uniformly located linear array. Unlike the classical MUSIC method, it is not needed for the method proposed in this paper to have the number of the signal sources known as a prior knowledge. The proposed method only needs the forward recursions of multistage Wiener filter (MSWF) to...
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